Culling Rasch Model Data

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The past posts have been concerned with how IRT analysis works when using different ways to estimate latent student ability locations and item difficulty locations. So far it seems that with good data a student ability location and an item difficulty location, at the same point on the logit scale, do represent comparable values. They will never make a perfect fit as that can only happen if the student ability and item difficulty distributions have means of 50% or zero logits and they have the same standard deviation or spread.

The perfect Rasch IRT model can never be completely satisfied. Winsteps, therefore, contains several features to remove data that “do not look right”. For this post, students and items more than two logits away from the bubble chart means were removed (that is more than about two standard deviations). The Fall8850a.txt file with 50 students and 47 items (no extreme values) was culled by 7 students and 7 items to 43 students and 40 items.

In both cases, rating scale and partial credit, culling resulted in lowering the standard error of locations (smaller bubbles). This improved the analysis. In both cases it also increased the estimated latent student ability and item difficulty locations. Getting rid of outliers, made the overall performance on the test look better.